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72-hour SOFA changes and risk stratification for invasive mechanical ventilation in patients with community-acquired Pneumonia
Abstract Accurate prediction of invasive mechanical ventilation (IMV) requirement in patients with community-acquired pneumonia (CAP) is a key clinical challenge. Conventional static risk stratification scores have limited value for 72-hour risk assessment, and the incremental prognostic value of dynamic organ dysfunction changes for IMV prediction remains to be systematically verified. This study aimed to evaluate the predictive value of 72-hour changes in the Sequential Organ Failure Assessment score ( $$\Delta$$ SOFA) for IMV requirement during hospitalization in CAP patients, and to develop a practical dynamic risk stratification tool designed for the 72-hour evaluation window. This was a retrospective cohort study based on the publicly available NACef database, which included 768 hospitalized CAP patients from a tertiary hospital in Colombia. After applying strict inclusion/exclusion criteria (hospital stay $$\ge$$ 72 hours, complete admission/72-hour SOFA scores and IMV outcome data), 581 patients were included in the final analysis. We constructed a dual-dimensional ABCD dynamic risk stratification framework based on admission SOFA score (<2 vs. $$\ge$$ 2) and 72-hour $$\Delta$$ SOFA trajectory ( $$\le$$ 0 vs. >0), stratifying patients into four subgroups: Low-Risk Stable (A), Low-Risk Deteriorating (B), High-Risk Stable (C), and High-Risk Deteriorating (D). Two logistic regression models (a static model with admission CURB-65, PSI and admission SOFA risk; a dynamic model adding $$\Delta$$ SOFA trajectory) were developed to predict IMV requirement. The predictive performance of the models was comprehensively evaluated in terms of discrimination, calibration, reclassification improvement and clinical utility, with internal validation via 10-fold cross-validation. The four ABCD subgroups exhibited significantly different IMV rates: Group A (5.8%, 4/69), Group B (33.3%, 13/39), Group C (30.3%, 106/350), and Group D (81.3%, 100/123) ( $$\chi^{2}$$ =136.90, $$P<0.001$$ ). Compared with Group A (reference), Group D had a markedly elevated adjusted odds ratio (aOR) for IMV (91.81, 95% CI: 28.47–375.09, $$P<0.001$$ ), followed by Group B (aOR: 12.60, 95% CI: 3.32–56.94, $$P<0.001$$ ) and Group C (aOR: 5.78, 95% CI: 2.07–20.99, $$P<0.001$$ ). The dynamic model achieved superior discriminative ability (AUC=0.850, 95% CI: 0.817–0.882) compared with the static model (AUC=0.741, 95% CI: 0.700–0.783), with a statistically significant improvement in AUC ( $$\Delta$$ AUC=0.109, $$P<0.001$$ ) . $$\Delta$$ SOFA worsening ( $$\Delta$$ SOFA>0) was independently associated with IMV (aOR: 13.77, 95% CI: 8.30–23.66, $$P<0.001$$ ). The dynamic model also showed better calibration (Hosmer–Lemeshow P=0.443, Brier score=0.149 vs. static model: P=0.732, Brier score=0.195), significant reclassification improvement (NRI=0.4824, IDI=0.1919, both $$P<0.001$$ ) and higher clinical utility (net benefit AUC=0.1287 vs. static model: 0.1063) across clinically relevant threshold probabilities (0–0.5). The optimal risk threshold for the dynamic model was 0.37, with a sensitivity of 65.5% and positive predictive value (PPV) of 77.2% for identifying high-IMV-risk patients. For hospitalized CAP patients with a hospital stay of $$\ge$$ 72 hours, $$\Delta$$ SOFA within 72 hours is an independent and valuable predictor of IMV requirement in CAP patients, with significant incremental prognostic value when added to conventional static risk scores. The ABCD dynamic risk stratification framework constructed based on admission SOFA and 72-hour $$\Delta$$ SOFA can effectively stratify CAP patients into distinct risk subgroups with clear IMV risk gradients, and the corresponding dynamic prediction model has excellent predictive performance and clinical utility. This framework provides a simple, operable dynamic risk assessment tool for the 72-hour clinical node, supplementing incremental IMV risk information for routine disease reassessment and supporting the paradigm shift in CAP risk assessment from single static admission evaluation to comprehensive “admission + dynamic trajectory” assessment.
Inhibition of Annexin A2 Facilitates PHB2-Mediated Mitophagy in Cardiomyocytes to Alleviate Cardiac Injury and Remodeling After Infarction
BACKGROUND: Mitophagy is critically involved in cardiac injury and repair after myocardial infarction (MI), whereas the annexin A family plays an important role in mitophagy. However, the intrinsic molecular underpinnings that orchestrate the homeostasis of mitophagy in the infarcted heart remain to be fully characterized. Here, we aimed to evaluate the role of ANXA2 (annexin A2) in cardiac mitophagy in response to MI. METHODS: Transcriptome analyses were conducted to identify differentially expressed genes and enriched pathways. Mitophagy, mitochondrial function, and cardiac injury and remodeling were analyzed in MI mice and neonatal rat ventricular myocytes with cardiomyocyte-specific Anxa2 knockdown or overexpression, as well as in models with Anxa2 knockdown combined with Phb2 (prohibitin 2) silencing. Immunoprecipitation, mass spectrometry, and glutathione S-transferase pull-down assays were used to identify the interacting proteins of ANXA2. RESULTS: We showed that ANXA2 was highly expressed in murine and human ischemic failing hearts, whereas increased circulating ANXA2 positively correlated with cardiac injury in patients with acute MI. Moreover, cardiomyocyte-specific Anxa2 depletion averted cardiac mitophagy inactivation, oxidative stress, cell death, and inflammatory cell infiltration, leading to significant improvements in infarct size, heart function, and cardiac remodeling after MI. Conversely, Anxa2 overexpression in cardiomyocytes suppressed mitophagy to exacerbate cardiac injury and deteriorate heart failure after MI. Moreover, Anxa2 silencing and overexpression, respectively, in neonatal rat ventricular myocytes under hypoxia in vitro recapitulated the in vivo findings on mitochondrial function and cell death. Mechanistically, we found that ANXA2 directly interacted with the mitophagy receptor PHB2 to competitively block the binding of LC3B (microtubule-associated protein 1 light chain 3 beta) with PHB2 and promote PHB2 proteasomal degradation through K48-linked polyubiquitination mediated by the E3 ligase TRIM29 (tripartite motif-containing 29), resulting in mitophagy inhibition under hypoxia. Consequently, Phb2 knockdown abrogated the protective effects of Anxa2 deficiency on mitochondrial function, oxidative stress, and cell viability in stressed myocytes in vitro, as well as on heart function and remodeling under MI in vivo. CONCLUSIONS: These findings highlight the significance of ANXA2 inhibition as a molecular brake on mitophagy inactivation in cardiomyocytes under MI and uncover an ANXA2-mediated posttranslational mechanism essential for maintaining mitochondrial homeostasis and alleviating heart failure after MI.
A generative explainable model for antimicrobial peptide prediction using bidirectional temporal convolutional neural network
High-Dose Versus Standard-Dose Influenza Vaccine and Cardiovascular Outcomes in Older Adults: The FLUNITY-HD Prespecified Pooled Analysis
BACKGROUND: The high-dose inactivated influenza vaccine (HD-IIV) has demonstrated superior protection against a range of hospitalization end points versus standard-dose inactivated influenza vaccine (SD-IIV), but its effectiveness against specific cardiovascular outcomes and in those with pre-existing cardiovascular disease (CVD) is not well elucidated. METHODS: In a prespecified secondary analysis of the FLUNITY-HD (Pooled Analysis of Methodologically Harmonized Pragmatic Randomized Trials of High-Dose vs. Standard-Dose Influenza Vaccine Against Severe Clinical Outcomes) individual-level pooled data set integrating 2 methodologically harmonized pragmatic, individually randomized trials conducted in Denmark and Spain, we investigated the relative vaccine effectiveness of HD-IIV versus SD-IIV against severe cardiovascular outcomes and according to pre-existing CVD among adults ≥65 years of age. Data were primarily obtained from routine health care databases, with follow-up from 14 days after vaccination to May 31 the following year. RESULTS: The pooled data set encompassed 466 320 individually randomized participants, of whom 107 700 (23.1%) had a history of CVD. HD-IIV reduced the incidence of hospitalization for influenza or pneumonia, cardiorespiratory disease, laboratory-confirmed influenza, and any cause compared with SD-IIV, irrespective of the presence or absence of pre-existing CVD ( P interaction >0.66 for all outcomes). Compared with the SD-IIV group, the HD-IIV group had a significantly lower incidence of hospitalization for any CVD (HD-IIV, 1.15%, versus SD-IIV, 1.24%; relative vaccine effectiveness, 6.6% [95% CI, 1.6–11.4]; P =0.010), hospitalization for any respiratory disease (HD-IIV, 0.92%, versus SD-IIV, 0.98%; relative vaccine effectiveness, 6.5% [95% CI, 0.7–11.9]; P =0.027), and hospitalization for heart failure (HD-IIV, 0.11%, versus SD-IIV, 0.15%; relative vaccine effectiveness, 21.3% [95% CI, 7.6–33.0]; P =0.003). CONCLUSIONS: In a prespecified pooled analysis of 466 320 individually randomized older adults, HD-IIV reduced the incidence of a wide range of severe cardiovascular and respiratory outcomes compared with SD-IIV, with consistent findings regardless of previous history of CVD. Among cardiovascular outcomes, the protective effect of HD-IIV versus SD-IIV was particularly pronounced against hospitalization for heart failure. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT06506812.
Resetting of quartz and feldspar luminescence signals under water
Abstract Quantifying luminescence signal resetting of sand grains in turbid waters is essential for both sediment dating and tracing, yet direct measurement under natural subaqueous conditions remain scarce. Here, we present the first depth-resolved experiment that combines in-situ luminescence resetting, subaqueous light spectra and suspended sediment concentration in a tidal inlet. Sand-sized quartz and feldspar grains were exposed to daylight at multiple depths during a one-day deployment, while optical and sediment conditions were continuously monitored. Single-grain luminescence measurements reveal depth-dependent resetting with a bleaching front below which no significant signal resetting occurs within a day. The position of this bleaching front depends on signal bleachability and agrees with predictions based on spectral irradiance and mineral-specific photo-ionization cross sections. By directly linking subaqueous light conditions, sediment concentration, and mineral-specific bleaching behaviour, our findings provide empirical quantification that can inform luminescence dating, provenance studies, and tracing of sediment transport in dynamic coastal systems.
Revisiting Digitalis in Heart Failure: Lessons From DIGIT-HF and Beyond
Impact of data space augmentation strategy on model accuracy and generalization in thin-section rock classification
Letter by Zou et al Regarding Article, “Impacts of Reducing Sitting Time or Increasing Sit-to-Stand Transitions on Blood Pressure and Glucose Regulation in Postmenopausal Women: Three-Arm Randomized Controlled Trial”
Phonological complexity, speech style, and individual differences influence ASR performance for Tarifit
Abstract This study examines individual differences through the lens of automatic speech recognition (ASR) transfer (applying ASR trained on one language to a new language) from Arabic to Tarifit, an under-resourced Amazigh language with typologically rare phonological structures. Thirty-seven native Tarifit speakers produced target words in both clear and casual speaking styles, allowing us to assess how phonological complexity and speech clarity interact to influence ASR performance. Results show that clear speech significantly improves recognition accuracy, particularly for words with rising sonority onset clusters. In contrast, falling sonority clusters and initial geminate consonants, which are both typologically marked structures, yield higher error rates even when spoken clearly. Importantly, we observe substantial speaker-level variability in ASR outcomes, though demographic factors such as age and gender do not predict performance. These findings suggest that individual differences in speech production and phonological encoding play a critical role in shaping ASR recognition success. By leveraging ASR as a proxy for perceptual processing, this work contributes to our understanding of how phonological structure and speaker variability jointly influence speech perception, with implications for inclusive ASR design and phonological theory.
Criteria to Assess the Predictive and Clinical Utility of Novel Models, Biomarkers, and Tools for Risk of Cardiovascular Disease: A Scientific Statement From the American Heart Association
Risk prediction has been used in the primary prevention of cardiovascular disease for >3 decades. Contemporary cardiovascular risk assessment relies on multivariable models, which integrate established cardiovascular risk factors and have evolved over time from the Framingham Risk Model to the pooled cohort equations to the PREVENT (Predicting Risk of CVD Events) equations. Recent scientific (ie, genomics, proteomics, metabolomics) and methodologic (ie, artificial intelligence) advances have led to a proliferation of novel models, biomarkers, and tools for potential use in risk prediction. In parallel, the growing armamentarium of preventive therapies, some with considerable cost, underscores the need for more accurate and precise risk assessment to prioritize those at highest risk who will derive the greatest absolute benefit. Accompanying the considerable enthusiasm for the potential of newer approaches to improve risk prediction is the need for rigorous evaluation and assessment of their performance (ie, accuracy, precision, incremental performance when added to contemporary multivariable risk models or established risk factors) and clinical utility (ie, actionability, scalability, generalizability) before adoption in clinical practice. Additional considerations in risk tool evaluation include reproducibility, cost–value considerations (including impact on downstream health care costs), and implications for health equity. This scientific statement defines a standardized framework for general considerations in risk prediction, statistical assessment of predictive utility, and critical appraisal of clinical utility and readiness. This scientific statement is intended to support clinicians, researchers, and policymakers in how best to evaluate current and emerging risk prediction tools and ultimately improve the prevention of cardiovascular disease in diverse populations.
Optimized topology control for large-scale IoT networks using graph-based localization
Abstract Internet of Things (IoT) is increasingly realized through large scale deployments of heterogeneous devices and gateways operating under strict energy budgets and interference limited links, which motivates reliability aware topology control and end to end communication performance objectives. As IoT deployments grow to massive scales and incorporate highly heterogeneous devices, designing and controlling network topology in a reliable and energy-efficient manner becomes a fundamental challenge. In particular, poor link quality, interference, and localization uncertainty severely limit the effectiveness of traditional topology-control approaches. In this paper, we address this challenge by introducing IoTNTop, a novel and unified graph-based framework for joint localization, graph embedding, and topology control in large-scale, resource-constrained IoT networks. Unlike conventional methods that decouple localization from topology design, IoTNTop embeds both end-nodes and gateways into a globally consistent spatial structure using partial and noisy distance measurements, and directly couples this geometry with communication-aware topology optimization. IoTNTop adopts an error-centric topology-control objective that explicitly minimizes end-to-end (E2E) error probability while enforcing practical code-rate and transmit-power constraints. The framework jointly optimizes link activation, transmit power, and data transmission code rate, and employs a scalable sub-graph stitching pipeline based on eigenvector synchronization (EVS), landmark alignment (LA), and semidefinite programming (SDP) refinement. A greedy signal-to-noise-ratio (SNR)–guided edge selection strategy with convergence checking further ensures computational efficiency. Comprehensive numerical analysis and network-level simulations show IoTNTop retains approximately 60–80% of the initial per-node energy budget while maintaining symbol error probability below 15% for the majority of nodes. At the same time, it converges in fewer iterations than Genetic Algorithm (GA) and brute-force baselines and sustains higher achievable code rates at lower transmit power levels. These performance gains remain consistent across the tested signal-to-noise ratio regimes and network sizes.
Clonal Hematopoiesis and Its Cardiovascular Implications: A Scientific Statement From the American Heart Association
Clonal hematopoiesis (CH), the benign clonal expansion of hematopoietic stem cells, is often caused by somatic sequence variations in genes associated with hematologic malignancies. Over the past decade, CH has emerged as a risk factor for a wide range of cardiovascular diseases (CVDs), including atherosclerosis, heart failure, atrial fibrillation, and thrombosis. The cardiovascular risk associated with CH is heterogeneous; it varies on the basis of specific genes and variants, clone size, and various extrinsic features. Mechanistic studies suggest that CH contributes to CVDs through both gene-specific pathways and broader inflammatory processes. These include aberrant cytokine production, inflammasome activation, and other proinflammatory mechanisms, which can accelerate atherosclerosis, promote thrombogenesis, and impair vascular or myocardial function. These findings underscore the importance of addressing CH as a potential contributor to CVDs. CH is predominantly considered an age-related phenomenon, but lifelong influences on the fitness of genetic variants, including germline predispositions, obesity, chronic inflammation, and exposure to environmental toxins (eg, tobacco, certain cancer treatments), influence CH. A greater understanding of CH risk factors is therefore important for both individual and population-level risk assessments. Incorporating CH-associated risk into existing CVD risk prediction models may inform new personalized preventive or therapeutic approaches. No CH-specific therapies have proven efficacy in CVD treatment or prevention, but multiple molecular-based therapeutic hypotheses are beginning to be tested.
Automated detection and segmentation of Weiss ring in fundus photography images using deep learning
Response by Kar to Letter Regarding Article, “Two-Year Outcomes of Transcatheter Edge-to-Edge Repair for Severe Tricuspid Regurgitation: The TRILUMINATE Pivotal Randomized Controlled Trial”
A new free surface identification method for 3D MPS method
Novel Plasma Proteomic Markers and Risk of Venous Thromboembolism
BACKGROUND: Venous thromboembolism (VTE) is a leading cardiovascular disease, yet its etiology is incompletely understood. This study used large-scale, high-throughput aptamer-based proteomics to identify new circulating protein biomarkers and biological pathways for incident VTE. METHODS: We included 4 longitudinal cohorts (the ARIC study [Atherosclerosis Risk in Communities], CHS [Cardiovascular Health Study], MESA [Multi-Ethnic Study of Atherosclerosis], and the HUNT study [Trøndelag Health]) that identified 1371 incident noncancer VTEs among 20 737 participants followed for a maximum of 10 to 29 years. We used the SomaScan to measure baseline plasma levels of ≈5000 to 7000 proteins and examined the prospective relationships between the protein biomarkers and noncancer VTE. We then conducted an external replication of top VTE proteins in 783 incident noncancer VTEs among 39 097 participants in the UKB study (UK Biobank) based on the Olink proteomics platform. We used Cox proportional hazards regression to estimate the association between each protein biomarker and VTE risk. Mendelian randomization (MR) analysis was used to assess the possible causal associations between identified proteins and VTE risk. RESULTS: There were 23 proteins that exceeded a false discovery rate–adjusted P <0.05 (unadjusted P <6.5×10 -4 ) in the discovery meta-analysis of ARIC, CHS, and MESA and were replicated in HUNT at (unadjusted) P <0.05. Of these, 15 are new to VTE, and 3 of the 15 (transgelin, sushi, von Willebrand factor type A, EGF and pentraxin domain-containing protein 1, and TIMP4 [metalloproteinase inhibitor 4]) exceeded the Bonferroni corrected significance threshold in HUNT. Sixteen of the 23 top VTE proteins were available on the UKB Olink panel, of which 11 were replicated in the UKB study after Bonferroni correction. MR analysis of the 15 new proteins provided significant evidence for a possible causal role of TIMD4 (T-cell immunoglobulin and mucin domain-containing protein 4) (Bonferroni-corrected P <0.05) and suggestive evidence for TIMP4 and CST3 (cystatin-c) (unadjusted P <0.05) in VTE risk. The direction of association from the MR analyses was opposite of that from the VTE proteomics analysis for TIMP4 and TIMD4 but was consistent for CST3. CONCLUSIONS: We identified several novel plasma proteins for VTE that reflect biological processes outside established VTE pathophysiology, including extracellular matrix regulation, immunity, immune–vascular endothelium interactions, and vascular senescence. Results may provide new modifiable targets to improve VTE risk stratification, prevention, or treatment.
Sixty years of observations and future projections of nine declining North American glaciers
Abstract Warming climate has greatly affected the stability of the world’s glaciers. In 1957, as part of the International Geophysical Year, nine North American glaciers were surveyed in great detail so that they could serve as future benchmarks. Since their initial survey, these nine benchmark glaciers have drastically decreased in volume and areal extent. This is a direct result of global warming. However, the exact amount of their change has never been fully documented and predictions of their lifespans in the wake of continued temperature increase has never been projected. In this study, we utilized advanced satellite techniques to quantify changes in the nine glaciers as well as assessed their long-term survivability. The methodology successfully used in this study can be applied to glaciers throughout the world, which would provide a cost-effective evaluation of past, present, and future glacial change at localized scales. We show that since 1957, the glaciers have lost 1.4 metric gigatons (Gt) of freshwater and have shrunk on average by 33%. Additionally, utilizing future temperature projections and cumulatively integrating temperature-driven volume changes over time, we show that only 3 of the 9 glaciers will still be in existence by 2100. These results paint a grim picture for the future generations observing glaciers in North America.